Melodis crystal decoder method and device

ABSTRACT

The technology disclosed relates to a system and method for fast, accurate and parallelizable speech search, called Crystal Decoder. It is particularly useful for search applications, as opposed to dictation. It can achieve both speed and accuracy, without sacrificing one for the other. It can search different variations of records in the reference database without a significant increase in elapsed processing time. Even the main decoding part can be parallelized as the number of words increase to maintain a fast response time.

RELATED APPLICATION

This application claims the benefit of U.S. Patent Provisional Application No. 61/117,065 filed on Nov. 21, 2008 and of U.S. Patent Provisional Application No. 61/110,547 filed on Oct. 31, 2008. These provisional applications are incorporated by reference.

This application is related to and incorporates by reference the following applications, which disclose technologies useful to processing speech:

“Pitch Dependent Speech Recognition Engine”, U.S. patent application Ser. No. 11/971,070 filed Jan. 8, 2008, which claims the benefit of 60/884,196 filed Jan. 9, 2007;

“Pitch Selection, Voicing Detection And Vibrato Detection Modules In A System For Automatic Transcription Of Sung Or Hummed Melodies”, PCT No. PCT/US08/82256 filed Nov. 3, 2008, filed in English and designating the United States, which claims the benefit of 60/985,181 filed Nov. 2, 2007;

“Pitch Selection Modules In A System For Automatic Transcription Of Sung Or Hummed Melodies”, U.S. patent application Ser. No. 12/263,812 filed Nov. 3, 2008, which claims the benefit of 60/985,181 filed Nov. 2, 2007;

“Voicing Detection Modules In A System For Automatic Transcription Of Sung Or Hummed Melodies”, U.S. patent application Ser. No. 12/263,827 filed Nov. 3, 2008, which claims the benefit of 60/985,181 filed Nov. 2, 2007; and

“Vibrato Detection Modules In A System For Automatic Transcription Of Sung Or Hummed Melodies”, U.S. patent application Ser. No. 12/263,843 filed Nov. 3, 2008, which claims the benefit of 60/985,181 filed Nov. 2, 2007.

BACKGROUND OF THE INVENTION

The technology disclosed relates to a system and method for fast, accurate and parallelizable speech search, a so-called “Crystal Decoder”. It is particularly useful for search applications, as opposed to dictation. It can achieve both speed and accuracy, without sacrificing one for the other. It can search different variations of records in a reference database without a significant increase in elapsed processing time. Even the main decoding part can be parallelized as the number of words increase to maintain a fast response time.

Speech interfaces have been an area of research for many years. A number of speech recognition engines have been created that work well. However, these systems have been optimized for dictation or automatic transcription. Their main task is to convert a speech waveform to a text transcription. Knowledge of context and use of statistical language models usually help with the accuracy.

There have been many attempts to apply transcription engines to the area of speech search. In such efforts, speech is first converted to text, and then text is sent to the search engine to retrieve the results. This system suffers from a number of weaknesses, mainly because search and dictation have their own unique challenges and a system that is designed for dictation is not necessarily optimized for search. For example, not knowing the context of the search engine can reduce the accuracy of the transcription stage. Then the error in the transcription will reduce the accuracy of the search engine. Another major problem is that search engines usually have a large number of words, which makes the decoder slow and inaccurate. In order to maintain high speed, the decoder then performs pruning which introduces additional error.

An opportunity arises to deliver components of a catalog search engine that responds to utterance of search requests. These components can be used separately or in combination. Better, readily parallelized and versatile voice analysis systems may result.

SUMMARY OF THE INVENTION

We disclose a system and method for fast, accurate and parallelizable speech search, called “Crystal Decoder”. It is particularly useful for search applications, as opposed to dictation. It can achieve both speed and accuracy, without sacrificing one for the other. It can search different variations of records in the reference database without a significant increase in elapsed processing time. Even the main decoding part can be parallelized as the number of words increase to maintain a fast response time. Particular aspects of the disclosed technology are described in the claims, specification and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a word HMM sequence. In this example, the word is composed of two triphones and each triphone is modeled by a three-state HMM.

FIG. 2 illustrates a word HMM sequence. In this example, the word is composed of two triphones and each triphone is modeled by a three-state HMM.

FIG. 3 shows a sample word graph.

FIG. 4 depicts sample two-state HMM's for the word “IT”.

FIG. 5 depicts a reference lattice.

FIG. 6 depicts system hardware and steps of a word spotting method.

FIG. 7 depicts system hardware and steps of a phrase spotting method.

DETAILED DESCRIPTION

The following detailed description is made with reference to the figures. Preferred embodiments are described to illustrate the present invention, not to limit its scope, which is defined by the claims. Those of ordinary skill in the art will recognize a variety of equivalent variations on the description that follows.

INTRODUCTION

Melodis' so-called Crystal Decoder is a new approach to speech search. it is an engine optimized for search applications and not for automatic transcription and dictation. In this system, voice is not converted to text blindly. Instead, an utterance is analyzed to find a desired catalog item.

Various embodiments of Crystal Decoder may have one or more of the following characteristics and/or advantages:

-   -   It does not need to use statistical language models to combine         words into phrases, reducing complexity.     -   It does not need to perform pruning of low scoring alternative         words or phrases to maintain speed.     -   It is a parallelizable decoder.     -   It can search large catalogs of millions of items with high         levels of speed and accuracy.     -   It can consider multiple variations of each record without         significant increases in running time.     -   Selection of items from a catalog becomes increasingly accurate         as the query gets longer. For example, users can say the entire         address “123 main st san francisco california” in one utterance         to find an address on a map.         The technology disclosed is very useful for natural language         search applications.         Crystal Decoder vs. Conventional Decoders

The following table summarizes characteristics and/or advantages, one or more of which are found in embodiments of the Crystal Decoder compared to other conventional systems.

THE CRYSTAL DECODER Crystal Decoder Other Conventional Decoders Voice to Text Does not need to convert voice to text 1: Convert voice to text Conversion first. It uses a sound based representation 2: Then use the text to search of the search utterance as input to the If there is an error in voice to text search engine, which may increase conversion, the search results will be accuracy. wrong. Pruning Does not need to perform any pruning When catalog size is large and the number such as beam or absolute pruning. All of words increases, pruning becomes possibilities can be considered. Speed necessary to keep the response time fast. and accuracy can be achieved together. Pruning sometimes eliminates the correct result and accuracy decreases. Therefore speed and accuracy cannot be achieved together. One has to be sacrificed for the other. Parallelizable Decoding can be parallelized to multiple Decoding is done in series frame by processors. Therefore, as the number of frame. All possibilities have to be words or catalog entries increase, use of considered together. Therefore it is not more processors can maintain speed. possible to parallelize the initial decoding Dictionaries of 500,000 to 1,000,000 process. If number of words is large, words can be accommodated using decoding will be slower. Therefore current processor configurations. applications limit the number of words to maintain speed. Language Models Does not use language models to Requires statistical language models to combine words into phrases. perform decoding. But language models are difficult to accurately predict in different usage scenarios and introduce complexity in the system. Query Length Gets even more accurate with longer They become less accurate when the queries. To find the address, users can query is longer. Therefore applications say the entire address at once: have to ask users to say short phrases What is the address: once at a time. For example: 123 Main St., San Francisco, What is your state: California California What city: San Francisco What street: Main St. What number: 123

Crystal Maker

The crystal making algorithm can be separated into “query” and “referenced” parts. The query is an utterance, which typically consists of an item to be selected from a catalog. The reference identifies an item in a catalog, against which the utterance is processed. One stage, which is the more involved one, creates an implicit graph or so-called crystal from the query or utterance. Another stage creates a graph for each of the references to be searched in response to the query. This second part can be done offline and used for multiple queries.

Variables Used in the Following Query Section

-   -   b_(—) transition log probabilities from state ‘i’ to state ‘j’         are denoted by ‘b_{i−>j}’     -   d certain log distribution     -   f frame     -   fef_(—) the first end frame of a word with a particular start         frame, fef_{w,sf_{w,i}}     -   i, j state indexes     -   i_s ‘i_s’ is the number of incoming states of ‘s’     -   l ‘l’ is the pronunciation length of ‘w’     -   lef_(—) the last contiguous end frame of a word with a         particular start frame, lef_{w,sf_{w,i}}.     -   m a number of states in an ‘m’ state hidden Markov model (HMM)     -   n number ‘n’ of frames     -   n score vectors v_s have lengths ‘n’ (v_{s,0}, v_{s,1}, . . . ,         v_{s,n−1}).     -   p word pronunciations ‘p_(—)0 p_(—)1 . . . p_{l−1}’ used when         creating a score table.     -   q number of entering states, sometimes denoted by s_(—)0, . . .         , s_{q−1}.     -   r total number of states in the word by ‘r’, indexed starting         with 0.     -   s_(—) incoming every ‘s’, and for all its incoming states         ‘s_(—)1’, . . . , ‘s_{i_s}’, s_(—)1<s, . . . , s_{i_s}<s, where         ‘i_s’ is the number of incoming states     -   sf_{w,i} A list of winning start frames for words in a         dictionary, sf_{w,i}.     -   u utterance ‘u’     -   v_s a vector of scores ‘v_s’ for state ‘s’     -   w a word ‘w’ in the dictionary     -   wf_{s,f} winning start frame ‘wf_{s,f}’ which for every state         ‘s’ and every frame ‘f’ keeps track of the starting frame that         led to the best score for that state in frame ‘f’.     -   x states emits a vector random variable ‘x’ according to a log         distribution ‘d’

Crystal Maker—Query

Consider a set whose elements can each be represented using a hidden Markov model (HMM) chain. We call this an HMM word set. Consider also a sequence of observations that have resulted from an HMM chain corresponding to a permutation of the elements of the set and possibly of some other set. We wish to find a set of candidate start points of each element in the sequence of observation and its corresponding probability.

For the purpose of concreteness, this set could be a dictionary of words. In this case, the goal of the crystal maker algorithm is to take an utterance and calculate a likelihood measure for the occurrence of all the dictionary words in that utterance. The algorithm also determines the most likely locations for the start of every word. The power of this algorithm lies in the fact that we do not necessarily need to have all the words that were uttered in our dictionary. With this algorithm, we can measure the likelihood that a given word in our dictionary appears in the utterance, irrespective of what other acoustic phenomenon may be present in the utterance. In addition, since words can be considered independently, this process can be distributed (parallelized) across multiple processors to reduce the latency of the system. This is different from other traditional methods that need to consider all the words together and perform decoding frame by frame to apply a language model. The traditional methods cannot be distributed easily on multiple processors.

Given an utterance ‘u’ containing ‘n’ frames and a word ‘w’ in the dictionary and its pronunciation ‘p_(—)0 p_(—)1 . . . p_{l−1}’ we create a score table. Here, ‘l’ is the pronunciation length of ‘w’.

In one embodiment, each triphone in the pronunciation of ‘w’ is modeled using an ‘m’ state hidden Markov model (HMM). The transition log probabilities from state ‘i’ to state ‘j’ are denoted by ‘b_{i−>j}’. Each state emits a vector random variable ‘x’ according to a certain log distribution ‘d.’. A sequence of HMM's is formed for ‘w’ by stringing together the HMM of every triphone inside ‘w’. We illustrate the HMM sequence as a directed acyclic graph. Acyclic means that if for two states ‘i’ and ‘j’ there is a non-zero probability of going from ‘i’ to ‘j’, then the probability of going from ‘j’ to ‘i’ is exactly zero.

Arrange the states of the HMM sequence in order so that for every ‘s’, and for all its incoming states ‘s_(—)1’, . . . , ‘s_{i_s}’, s_(—)1<s, . . . , s_{i_s}<s, where ‘i_s’ is the number of incoming states of ‘s’. FIG. 1 illustrates a word HMM sequence. In this example, the word is composed of two triphones and each triphone is modeled by a three-state HMM.

For every state ‘s’ in the HMM sequence, we allocate a vector of scores ‘v_s’. This score vector has length ‘n’(v_{s,0}, v_{s,1}, . . . v_{s,n−1}). Since the HMM sequence is an acyclic graph, there are states in the sequence which have no incoming states. Denote these entering states by s_(—)0, . . . , s_{q−1}. One of the reasons that there may be multiple entering states is that we have multiple left contexts at the beginning of the word. For instance, the word ‘world’ may be preceded by ‘hello’, in which case the first triphone of ‘world’ is ‘OW [W] ER’ (using the CMU Sphinx phonetic set). It may be preceded by ‘my’, in which case the first triphone of ‘world’ is ‘AY [W] ER’. These two triphones may have different state sequences. Thus, the start states of these two triphones are in the set of entering states for ‘world’. Note here that the exit states of these two triphones will become the incoming states of the start state of the second triphone in ‘world’—namely ‘W [ER] L’.

Denote the total number of states in the word by ‘r’. Each word may have a single or multiple exit states. These are states that are not incoming to any state in the word HMM sequence. Note that here state ‘r−1’ is an exit state. We are interested in the score of the word ‘w’ in state ‘r−1’ (and in general in all exit states) for every frame.

Also, define a winning start frame ‘wf_{s,f}’ which for every state ‘s’ and every frame ‘f’ keeps track of the starting frame that led to the best score for that state in frame ‘f’.

If we are not interested in keeping track of a particular exit state but are only interested in the best possible score to reach any word exit and its corresponding winning start frame, we can tie the exit states together. For instance, we can append a silence phoneme at the end of ‘w’ and let the exit state of the silence phoneme be the same as the exit state of ‘w’ without silence. The exit state score in every frame will then give us the score of ‘w’ with or without silence, whichever is greater, in the following pseudo-code:

Initialization /* Go over all the frames */ for every frame f = 0 to n − 1   /* Go over the starting states */   for state s = 0 to q − 1    v_{s,f} = 0    wf_{s, f} = f   end   /*    * Preset the exit scores to keep track of whether the exit    * state is reachable in a particular frame.    */   v_{r − 1,f} = −infinity   wf_{r − 1, f} = −1 end

Table Computation for every frame f = 1 to n − 1   for every state s = q to r − 1    /* t_0 is the first incoming state into s */    v_{s,f} = v_{t_0, f − 1} + b_{t_0->s} + d{t_0, f}    wf_{s,f} = wf_{t_0, f − 1}    /* i_s is the number of incoming states into s */    for every incoming state of s, t = t_1 to t_{i_s − 1}     score = v_{t, f − 1} + b_{t->s} + d{t, f}     if(score * (f − wf_{s,f}) > v_{s,f}*(f − wf_{t, f − 1}))      v_{s,f} = score      wf_{s,f} = wf_{t, f − 1}     end /* if */    end /* for every incoming */   end /* for every state */ end /* for every frame */

The reader may recognize this as a modified viterbi algorithm. Every cell in the table is specified by an HMM state and a frame number. Before comparison, the score at every cell is normalized by the frame minus the winning start frame that led to that state. However, the raw score is kept throughout while computing the table. The normalization can be used to reduce dimensionality in computation. Without normalization, a new score table is required for every frame.

After this computation for every frame ‘f’ in 0, . . . , n−1, we will have two vectors for the exit state ‘r−1’:

v_{r−1, f}

wf_{r−1, f}

The first one indicates the best acoustic score of the word exiting in frame ‘f’. The other is the start frame that resulted in that end frame score in frame ‘f’.

Post Processing

Here we scan through the winning start frame array for each word ‘w’ and produce the following information:

A list of winning start frames for every word, sf_{w,i}. Optionally, we may eliminate start frames whose normalized scores are less than a fixed threshold. The threshold can be determined using statistical modeling and may or may not depend on the properties of the word such as its length. This will reduce the set of words that will be considered later in the crystal filter.

A mapping from every start frame in the above list to the last contiguous end frame of that start frame, lef_{w, sf_{w,i}}.

A mapping from every start frame in the above list to the first end frame of that start frame, fef_{w,sf_{w,i}}

The result aboves implicitly form a graph or a crystal for the utterance in the query. Each node in the crystal is defined by a word ‘w’ and a start frame for ‘w’. There are no explicit edges. As we will see in the next section, the edge scores will be computed on the fly as every reference graph is considered.

Memory and Computation Efficient Storage of Word HMM's

We run the table computation for every word in the dictionary. However, many words share the same starting letters. For instance, the words ‘jump’ and ‘jumping’ have the same starting three triphones. In a three-state HMM model depicted in FIG. 1, the two words share at least the first 9 states. We are then able to share computation if we represent the entire dictionary by a tree. The root of the tree will point to all distinct states (or phonemes or other sound-based representations of words) that start all the words in the dictionary. If two words have the same start state ID, they will share the child of the root corresponding to this state.

If two words share the same initial two states then they will share the first two levels in the tree. We keep constructing the tree in this manner so that the tree will represent all the dictionary words. A given node at a given level represents a set of all the words that share all the ancestors of this node. The leaves of the tree represent all the words that have the exact same HMM sequence.

Given the tree structure, we will apply the table computation algorithm to the nodes of the tree one level at a time. This way a sequence of states that is shared at the beginning of multiple words will only be computed once. An exemplary algorithm follows:

Loop over all the levels in the tree   Loop over all the states in this level    Compute the score vector for the state as in the algorithm above   End /* Loop over all states */ End /* Loop over all levels */ The leaves of the tree now have all the information we need for performing the steps in the Post Processing section above.

Working Example

While the algorithm above is complete, it may be easier to understand in the context of a working example. Consider the word ‘IT’ in the dictionary. The pronunciation for IT is:

IT>IH T

Assume a silence left and right context:

SIL IH T SIL

Therefore, the triphones are:

T1: SIL IH T T2: IH T SIL

Let's use a two state HMM for each triphone. We pick these triphone states from a code book that is computed a priori—i.e., each triphone maps to a fixed set of states:

Triphone name 1^(st) State Index 2^(nd) State Index T1 10 15 T2 22 23 The numbers above are completely arbitrary. Normally there are 6000-8000 distinct audio states that are used to build all the triphones in the English language. The two-state HMM's are depicted in FIG. 4.

We assign a (−1) to each of the transition probabilities in the HMM for the purpose of this example. However, note that in general the transition probabilities are different.

Assume there are 8 frames of audio. We have already precomputed a table of acoustic scores for each frame for each state. This portion is independent of the words in the dictionary.

Table of acoustic scores for each of the states in ‘IT’:

states 1 2 3 4 5 6 7 8 10 −7 −8 −1 −5 −9 −10 −11 −8 15 −10 −10 −7 −6 −2 −4 −7 −12 22 −4 −15 −12 −8 −7 −3 −1 −2 23 −14 −11 −9 −10 −13 −9 −4 −1

We wish the compute the score table for ‘IT’. Here is the score table:

1 2 3 4 5 6 7 8 10 score(1, 1) =  −8, 2  −1, 3  −5, 4  −9, 5 −10, 6 −11, 7  −8, 8 −7, 1 15 NR −18, 1 −16, 2  −8, 3 −11, 3 −16, 3 −24, 3 −37, 3 22 NR NR −31, 1 −25, 2 −16, 3 −15, 3 −17, 3 −20, 3 23 NR NR NR −42, 1 −39, 2 −26, 3 −20, 3 −19, 3 exit NR NR NR NR −43, 1 −40, 2 −27, 3 −21, 3 NR means non-reachable. The first column is the sequence of the state indices introduced above. The top row is the frame number.

We initialize the top row as follows. We set the score for every column to be the acoustic score of state 10 in that frame. The start frame (the number after the comma) is set to be the frame itself.

In order to compute the score in row 2 column 2, denoted by score(2,2):

-   score(2,2)=score(1,1)+acoustic_score(2,2)+transition_score(10 to     15)=−7+(−10)+(−1)=−18 -   start_frame(2,2)=1 -   Similarly     score(3,3)=score(2,2)+acoustic_score(3,3)+transition_score(15 to     22)=−18+(−12)+(−1)=−31

We are now ready to compute score(2,3). This is the cell for row state 15, column sample 3. There are two possible ways to get to this cell from column sample 2, row states 10 and 15. One is from cell(1,2): this represents the transition from state indexed 10 to the state indexed 15. The other is from cell(2,2): this represents the transition from state indexed 15 to itself.

-   Score from the parent cell(1,2) is:     s1=score(1,2)+acoustic_score(2,3)+transition_score(10 to     15)=−8+(−7)+(−1)=−16

This transition assumes that the start frame is frame 2. Denote this start frame by sf1.

-   Score from the parent cell(2,2) is:     s2=score(2,2)+acoustic_score(2,3)+transition_score(15 to     15)=−18+(−7)+(−1)=−26     This transition assumes that the start frame is 1. Denote this start     frame by sf2.

Note that in the above computation the start frame is inherited from the parent cell. Now it is time to compute the winning parent cell.

score_norm(2,3)=maximum of {s1/(f−sf1+1) and s2/(f−sf2+1)}={−16/(3−2+1), −26/(3−1+1)} score_norm(2,3)=maximum of {−8, −8.67}=−8. Thus the best score to get to cell(2,3) is −8 and the best way to get there is through cell(1,2) whose start frame is 2. Therefore: score(2,3)=−16 start_frame(2,3)=2(inherited from cell(1,2))

Notice how the two start frames (namely 1 and 2) are competing for the best score. This is only possible because we normalized the scores by the number of frames. Similarly to compute score(2,4) first we pick the parents: cell(1,3) and cell(2,3)

score_norm(2,4)=maximum of {(−1+(−6)+(−1))/(4−3+1) and (−16+(−6)+(−1))/(4−2+1)}=maximum of {−4, −7.67}=−4 Thus winning parent is cell(1,3) score(2,4)=−8 start_frame(2,4)=3 (inherited from cell(1,3))

After we reach the final state of ‘IT’ and fully compute the bottom row, we go through this row and pick the distinct start frames and their score:

score corresponding to best normalized end frame start frame score 5 1 −43 6 2 −40 8 3 −21 The scores for start frames 1 and 2 are very low and we can remove them from the list of candidates. We are thus left with start frame equal to 3. Usually for a given query of length 5 seconds we end up with an average of 5-10 start frame candidates per word in the dictionary.

In this working example, the sound-based representation uses periodic acoustic samples translated into acoustic states that are processed using at least one hidden Markov model. The hidden Markov model is used to represent the utterance and words in the dictionary. The method described is used to identify candidate word locations at arbitrary positions substantially throughout the utterance.

Other sound-based representations can be used. For instance, words can be represented directly as sequences of phonemes. The utterance can be a processed to generate a series of phonemes using any desired acoustical analysis. For instance a phoneme graph can be constructed using methods described in M. Ravishankar, Efficient Algorithms for Speech Recognition, Ph.D Thesis, Carnegie Mellon University, May 1996, Tech Report. CMU-CS-96-143. Or, one can generate the graph by the methods described in C. M. Westendorf, J. Jelitto, “Learning pronunciation dictionary from speech data”, Fourth International Conference on Spoken Language, 1996. ICSLP 96 Proceedings. These two papers are hereby incorporated by reference. Applying these or other acoustical analyses, phonemes detected in the utterance will have at least one starting frame and may have a series of potential ending frames. The potential ending frames may be represented by a first and last ending frame. Phonemes can be used as the sound-based representations of the utterance and the words in the dictionary. Then the previously described word locating process would use the phonemes instead of acoustical states and hidden Markov models. As with the HMM, words represented by phonemes can be scored against potential starting locations in the utterance.

Variables Used in the Following Reference Section

-   -   </s> graph have an exit node </s>     -   <s>graphs have a start node <s>.     -   d_ a deletion penalty, denoted by ‘d_{wid}’, assigned to a graph         edge that bypasses an element. The penalty may proportional to         the degree of acoustic information inherent in the word.     -   d_(—) p minimum penalty: The penalty on the edge connecting node         ‘i’ to node ‘j’, denoted by ‘d_{i,j}’ is: minimum of d_p over         all ‘p’.     -   g graphs ‘g’ contain a sequence of nodes ‘n’=0, . . . , m−1.     -   i, j catalog item graphs are modified to allow for the deletion         of words in the graph. Edges connect a node ‘i’ in the graph to         another node ‘j’, bypassing the deleted node.     -   p path over which penalty d_p is calculated     -   wid_(—) visited nodes between node i and node j.

Crystal Maker—Reference

References to catalog items are stored as graphs. The words in phrases that reference catalog items are the nodes of the graph. In one embodiment, the words are elements in an HMM word set. In other embodiments, the words may be represented by phonemes, without HMMs. Two words that follow each other are connected by an edge. To use a specific notation, each graph ‘g’ contains a sequence of nodes ‘n’=0, . . . , m−1. Again, as with the HMM graphs above here assume the nodes are ordered so that for every node ‘n’, all its incoming nodes, ‘n_(—)0’, . . . , ‘n_{i_n}’ are less than ‘n’. Each node has a word ID, ‘w_n’ associated with it.

Each graph has a start node denoted by <s>. This is an entering node and has no incoming nodes. The graph also has an exit node </s> which is not incoming to any node in the graph. All the final words in a catalog item are connected to </s>. Thus </s> has a single start frame equal to the last frame in the query. FIG. 3 shows a sample word graph.

Catalog item graphs may be modified to allow the deletion of any sequence of words in the graph. To do this, we place an edge from any node ‘i’ in the graph to any other node ‘j’ that bypasses deleted nodes. We assign a penalty to this edge as follows. First, we assign a penalty to every word in the dictionary. In one embodiment, the penalty is proportional to the degree of acoustic information inherent in the word. Denote this by ‘d_{wid}’. Suppose, on a sample path from ‘i’ to ‘j’ we visit ‘wid_(—)1’, ‘wid_(—)2’, . . . , ‘wid_(n)’. Then, the penalty for this sample path indexed by ‘p’ is given by:

d _(—) p=d _(—) {wid _(—)1}+d _(—) {wid _(—)2}+ . . . +d _(—) {wid _(—) n}

The penalty on the edge connecting node ‘i’ to node ‘j’, denoted by ‘d_{i,j}’ is: minimum of d_p over all ‘p’. FIG. 2 shows an example of a deletion edge with the curved dashed line. This edge would allow for the deletion of the first ‘goo’ in the graph. The cost associated with this edge is preset deletion cost of the word ‘goo’.

For every graph, the crystal filter algorithm scores every valid path from <s> to </s>. Each such path represents a way that the user can search for this catalog item. For instance the user might say “Goo Goo Dolls” or they might say “The Goo Goo Dolls”. In both these cases the user would be referring to the same band. This is why the catalog item is, in general, a graph and not a sequence of words. The crystal filter algorithm takes the output of the crystal maker in order to determine these path scores.

Alternatively, deletion penalties could be calculated based on the power of the deleted word to distinguish among catalog items, such as using relative frequency of a word among phrases in the catalog. This alternative builds upon a standard approach to text retrieval.

Reference graphs may be created as follows:

Enumerate all the possible ways that a user can say that reference. For instance, “Coffee Shop in San Francisco”, “Coffee Shop in San Francisco, Calif.” and “Jim's Coffee Shop in San Francisco, Calif.” refer to the same item in the catalog. The first and the second alternative refer to a superset of the third alternative. Nevertheless, if a user says any of these three, “Jim's Coffee Shop in San Francisco, Calif.” must be in the result set that is returned by the speech search system.

Take all these possibilities and form a minimal edge graph using existing off-the-shelf algorithms.

Optionally, add a popularity score (e.g. probability) to each path.

The optional use of these graphs to represent phrases that can be used to select an item from the catalog is accurate and comprehensive, as it enumerates all the possible ways a user can select an item. It is fast since these possibilities are stored as a graph with minimum number of edges. This avoids need for start frame pruning. Its time complexity is below order ‘V’, where ‘V’ is the total number of edges in all the reference graphs. Thus, the search complexity will grow sublinearly as the number of phrases to search grows.

Crystal Filter

In the Crystal Maker described above, we measured the utterance likelihood of every word in the dictionary and found the words' most likely positions. We now need a way to calculate the likelihood that the utterance selecting particular catalog items, so that we can sort the catalog items in terms of relevance.

The Algorithm

We begin by defining a match history table. The match history table is used to store the necessary information for the best way to arrive at a given start frame of a word. Here is what we need. Define a history table structure:

node_ID ‘n’ in the reference graph word_ID score: Score of the match up to the start frame of this match history row. start_frame: Start frame of the ‘word_ID’ above of this match history row. the first end frame for this word_ID for this start_frame, first_end_frame history table index of the previous best match, best_match_index number of words matched so far, best_match_length

Before starting the matching algorithm for a particular reference, we need to initialize the match history table. Every reference graph starts with the ‘<s>’ symbol. By definition we set the start frame of this symbol to frame 0—the very first frame. We set the score to zero and the first_end_frame to zero as well. This means that any word can now be connected to ‘<s>’ starting in the first frame. We insert the following information as a row in the match history table:

node_ID of <s> = 0 word_ID = word ID of <s>, the start tag score = 0 start frame = 0 first_end_frame = 0 best_match_index = −1 best_match_length = 0

Now that we have the initial condition, we can start going through the rest of the reference nodes in the graph. The following block of code essentially finds the best path to get to every start frame of every word in the reference graph in an efficient manner. The score is also computed for every such path.

For every node in the catalog item graph n=0 to m−1

 w = w_n  /* m_w is the number of start frames of w */  /* Remember also that if w = </s> then sf = last speech frame in the utterance */  for every start frame of w, sf = sf_{w,i}, i = 1,..., m_w   set score = −infinity   /* This will be the score of getting to node ‘n’ starting in frame ‘sf’ */   for every incoming node of n, k = n_0, ..., n_{i_n}    /* Since the first_end_frame list for every query     * node k is sorted the following list can be quickly accessed:     */    for every history table row corresponding to node k, ht_k     if(first_end_frame of ht_k < sf)      /* Compute the score of the word leading to w_n       * We shall describe the acoustic_score function       * later below.       */      query_transition_cost = acoustic_score(ht_k.word ID, ht_k.start_frame, sf)      ref_transition_cost = d_{ht_k.node_ID, n}      /* This is precomputed for all references as explained above */      if(score < query_transition_cost + ref_transition_cost + ht_k.score)       score = query_transition_cost + ref_transition_cost + ht_k.score       best_match_index = ht_k       best_match_length = ht_k.best_match_length + 1      end /* if */     else      break out of for ... ht_k     end /* if(start_frame of ht_k ... */     end /* for ... ht_k */    end /* for ... k */    if(score > −infinity)     create a new history table structure new_ht     new_ht.node_ID = n     new_ht.word_ID = w_n     new_ht.start_frame = sf     new_ht.first_end_frame = fef_{w_n, sf}     new_ht.score = score     new_ht.best_match_index = best_match_index     new_ht.best_match_length = best_match_length    end /* if */   end /* for ... sf */ end

Applying this algorithm, the catalog_item_score=score of the last element in the match history table.

In this algorithm, reference to the function acoustic_score(word_ID, sf, ef) means

  if(wf_{exit state, ef} == sf)    return v_{exit state, ef}   end /* if */   /* See the definition of lef in the crystal making algorithm above */   lef = lef_{word_ID, sf}   delta = ef − lef   /* Below frame_deletion_penalty is a tunable negative parameter */   return v_{exit state, lef} + frame_deletion_penalty * delta end

This score is then used to rank the items in the catalog. Note that here we have computed a rough score for every catalog item graph. Suppose the crystal maker determines that a word starts in frame ‘f’. The final probability of the word ignores the viterbi history of the utterance before this frame. This, however, does give us a rough measure of the word score. Once we rank the crystal filter results we can take the top ‘N’ candidates and perform a more refined scoring on these candidates. ‘N’ here is chosen so that with high probability the desired catalog item is included in the list of top ‘N’ candidates. FIG. 3 illustrates a sample word graph. The dashed link means that we allow the deletion of the first ‘goo’. A deletion cost is associated with following edges that delete words.

Working Example of the Crystal Filter

The so-called Crystal Filter can be applied in a simplified example. For the purpose of this example we choose the reference lattice depicted in FIG. 5. In FIG. 5, the dashed line represents the fact that the word ‘My’ may be deleted with a penalty of ‘−40’. The solid lines have zero penalties.

Assume the audio is 100 frames. Suppose also after the Crystal Maker has run, we will have the following information for the words ‘My’ and ‘Immortal’:

Start and end frame information for the word ‘My’ Start frame First end frame Last end frame 10 20 30 25 41 50 60 72 100

Start and end frame information for the word ‘Immortal’ Start frame First end frame Last end frame 5 40 65 46 75 100

In addition to this information, we have available the score of the words starting in a particular frame and ending in any frame. For instance, the score of ‘Immortal’ starting in frame 46 and ending in frame 80 is −45. We also have the score of a filler word (e.g. silence) starting in frame zero and ending in any given frame.

We create the match history table with the following fields and insert the start tag into it:

Match Match Node_ID Word_ID Score Start_frame history Length 0 <s> 0 0 −1 0 For this example, we will use the word itself as the word ID. However, computers more typically map words to integer indexes starting from zero. These integer ID's are easier to use that the words themselves.

We now go over the inner words of the reference, as illustrated in the table below. For Node_ID n=1, the word is ‘My’. Its alternative Start frames are 10, 25, and 60. We now intend to find the best way to get to all the start frames of ‘My’.

sf=10, sf_{'My', 1}, the first start frame of ‘My’. Initialize the score=−infinity The only incoming to the word ‘My’ is ‘<s>’.

Go through all the rows in the match history table corresponding to ‘<s>’. There is only one such row. Call it mht_row. Is the first end frame of <s> before sf=10? Yes. Thus,

query_transition_cost=acoustic_score(<s>, 0, 10), which we in this example to be −25. reference_transition_cost=score of the solid arrow going from ‘<s>’ to ‘My’. new_score=query_transition_cost+reference_transition_cost+mht_row.score=−25+0+0 Since ‘new_score’>‘score’ (which we initialized to −infinity) we set ‘score’ to ‘new_score’: score=−25. best_match_index=0 where best_match_index is the index of the match history table that resulted in the best score.

We are done with all the match history rows of all the incoming nodes of ‘My’ (there was only one). Since score is not ‘−infinity’, we can insert a new match history row for sf=10 of ‘My’:

Match Match Node_ID Word_ID Score Start_frame history Length 0 <s> 0 0 −1 0 1 My −25 10 0 1 1 My −30 25 0 1 1 My −80 60 0 1

Similarly we have filled the another two rows by assuming that ‘<s>’ ends in frame 25 with a score of −30 and ends in frame 60 with a score of −80.

Next, for Node_ID n=2, the word is ‘Immortal’. Its start frames are 5, 46. sf=5, sf_{'Immortal', 1}, the first start frame of ‘Immortal’.

Again, set score to −infinity. Go over the incoming nodes of node ‘2’. The first one is ‘<s>’, node 0. Go over all the match history table items corresponding to ‘<s>’. There is only 1. query_transition_cost=acoustic_score(<s>, start frame=0, end frame=5) (assume this is −15) new_score=query_transition_cost+ref_transition_cost+mht_row.score=−15+(−40)+0=−55 Since ‘score’<‘new_score’, set score=−55 best_match_index=0

The second incoming of ‘Immortal’ is ‘My’, node 1.

Go over all the match history items corresponding to node 1. There are 3 corresponding rows. For these rows, the first end frame of ‘My’ (20, 41, and 72) is greater than the start frame of ‘Immortal’ under consideration, 5. Thus they will have a −infinity acoustic score.

Thus for ‘sf’=5 of the word ‘Immortal’ we get the following match history table:

Match Match Node_ID Word_ID Score Start_frame history Length 0 <s> 0 0 −1 0 1 My −25 10 0 1 1 My −30 25 0 1 1 My −80 60 0 1 2 Immortal −55 5 0 1 n=2, sf=46, sf_{‘Immortal’, 2}

The possible incomings are the three rows below the title. The scores for:

match history index 0: new_score=acoustic_score(<s>, start frame=0, end frame=46) (assume −70)+reference_transition_cost+mht_row[0].score=−70−40+0=110 match_history_index 1: new_score=acoustic_score(My, start frame=10, end frame=46)+reference_transition_cost+mht_row[1].score

Now note from the table above for the word “My”, the last end frame of ‘My’ corresponding to the start frame 10 is 30 which is less than 46. Here, the acoustic_score method will return the score of ‘My’ starting in frame 10 and ending in frame 30 (assume −80) plus (46−30)*(deletion_penalty), where deletion_penalty is a tunable parameter say −5 per frame.

Thus, ‘acoustic_score(My, start frame=10, end frame=46)=−80+16*(−5)=−160

new_score=−160+0−25=−185 match history index 2: new_score=acoustic_score(My, 25, 46)+reference_transition_score+mht_row[2].score=−40+0−30=−70

Thus the best score for the start of ‘Immortal’ at frame 46 is −70, and the best way to get there is through row index 2. This new row is the next to bottom row in the table below.

We similarly can fill out the rest of the table:

Match Match Node_ID Word_ID Score Start_frame history Length 0 <s> 0 0 −1 0 1 My −25 10 0 1 1 My −30 25 0 1 1 My −80 60 0 1 2 Immortal −55 5 0 1 2 Immortal −70 46 2 2 3 </s> −110 100 5 3

We assumed that:

acoustic_score(‘Immortal’, 5, 100)=−300, note here that we need to delete frame 65 through 100 for this case. acoustic score(‘Immortal’, 46, 100)=−40 Also, note that ‘</s>’ is treated like a word with a single start frame equal to the number of frames in the audio.

We see that the score of matching the reference in the table above is −110. We can also back trace through the match history table using the match history column and find the best path:

<s>, start frame = 0 My, start frame = 25 Immortal, start frame = 46 </s>, start frame = 100

Crystal Align

In order to determine a more refined likelihood measure for the list of top ‘N’ candidates, we run a final alignment on every one of these candidates. For this part of the system we can simply use a standard HMM alignment program such as used in CMU's Sphinx implementations. We use these refined likelihood measures for the final ranking Optionally, the popularity score of the records, and/or the paths in each record, can be used to adjust the final scores.

Some Particular Embodiments

The present invention may be practiced as a method or device adapted to practice the method. The same method can be viewed from the perspective of word spotting, phrase spotting or both. The invention may be an article of manufacture such as media impressed with logic to carry out computer-assisted word spotting, phrase spotting or both.

The technology disclosed may be applied in a variety of methods and successive methods. It also may be incorporated in systems or devices. Some devices that apply the technology disclosed are computers combined with software. Others are articles of manufacture, including computer readable storage media loaded with instructions they can be combined with a processor to carry out the methods. Alternatively, a computer readable transmission media may convey instructions that can be combined with the processor to carry out the methods.

One method that applies the technology discloses a method of electronically processing an utterance to locate candidate words at arbitrary positions 624, 626 within the utterance 621A. FIG. 6 depicts aspects of this word spotting method. This method includes dividing a dictionary 613 of word representations into multiple word sets 613A-C. The dictionary may be stored in a rotating or non-rotating memory 611. The word sets can then be processed in parallel. The word representations optionally may be based on hidden Markov models (FIGS. 2, 4), phoneme representations, or other conventional representations of words that are used in automated sound recognition. When hidden Markov models are used, the HMMs may represent phonemes or may directly represent words, without intermediate phoneme representations.

This first method continues with searching and scoring for particular word sets. It involves searching the utterance 621A for likely instances of each word representation at locations in the utterance 624, 626 that overlap in time 621B. The utterance 621A electronically represents a passage of speech. The utterance may be divided into frames, phonemes or using another conventional representation of speech from the art of automated sound recognition. Likely instances of word representations overlap as generally depicted in FIG. 6, 621B. Each likely word instance 624, 626 is scored with a probability of a match between the word representation and a particular location in the utterance.

The searching and scoring are performed on multiple processors 615A-C, each operating on a respective one of the multiple word sets 613A-C.

This method usefully reports at least a subset of likely word instances 621B and respective probability scores for further electronic processing.

In some trials, it has been observed that the number of likely word instances reported may be five or six times the number of words in the dictionary. With tighter cut-offs for reporting, the number of likely word instances reported may be reduced to three times as many as the number of words in the dictionary. Or it may be reduced even further. The trade-off that needs to be considered is between reducing the amount of filtering during phrase spotting that follows the word spotting, and keeping the likely word instance subset large enough that the speaker's intended words are not eliminated before phrase spotting.

The further electronic processing may typically include filtering and alignment. The filtering is described as a successive method that can be combined with this first method or practiced separately. The alignment practices a conventional technique, such as described at Carnegie Mellon University's website for the Sphinx project.

One aspect of the technology disclosed includes representing a likely word instance in a data structure that includes a start frame, a first end frame, a last end frame. When the first and last and frames are different, multiple probability scores are assigned to varying lengths of segments in the utterance.

The dynamic programming methods described above can be applied to the searching and scoring. These dynamic programming methods include Viterbi and modified Viterbi methods. Note that a dynamic programming method can accomplish the searching and scoring in a single pass.

Normalizing is useful when using dynamic programming, because dynamic program scoring typically accumulates partial scores as a table of possible matches between source and target sequences is traversed. In some instances, such as slow and halting speech, matches to longer parts of the utterance may have unfavorable scores, because they accumulate to less favorable scores. One way to normalize is to divide a score calculated by the dynamic programming method by the number of frames in the matching segment of the utterance. This facilitates comparison of likely word instances that match shorter and longer passages of the utterance. A number of other normalization approaches could be substituted for using the number of frames, such as a function of the number of frames. The function might be exponential, logarithmic, linear, quadratic or a similar monotonic function. Normalizing can be applied to either HMM-based or phoneme-based representations and analysis.

A phoneme representation of an utterance can be based on phoneme graphs constrained by a language model. The language model preferably would use the word representations in the dictionary. It might also use phrases from a catalog. The language model can be used to create a phoneme graph of reduced density, by only allowing transitions that actually occur in the dictionary and/or catalog. A language model-constrained phoneme graph can then be trained against audio data. The audio data may represent alternative pronunciations by more than 100 speakers or by more than 1000 speakers. Transition probabilities for edges of the phoneme graph may be derived by training from the audio data, by language model analysis of the text in the dictionary and/or catalog, or by combining analysis from both sources.

Parallel processing can be further extended to subdividing the utterance 621A into segments overlapping in time 631, 633. The overlap should assure that a long word will not be bisected and made unrecognizable.

A successive method, usefully combined with the word spotting above, involves phrase spotting. FIG. 7 depicts this phrase spotting method. Phrase spotting matches the likely word instances to catalog entries. It includes dividing a catalog of phrases 763 that represent items into multiple phrase sets 763A-C. The catalog may be stored in a rotating or non-rotating memory 761. The phrase sets can then be processed in parallel by multiple processors 765A-C. The method continues with searching and scoring particular phrase sets 763A-C. It involves searching a set or reported subset of likely word instances 621B for likely occurrences of each phrase 774, 776. Each likely phrase occurrence is scored for probability of a match between the phrase 763 and the utterance 621B. The dynamic programming methods described above can be applied to the searching and scoring. The searching and scoring are performed on multiple processors 765A-C, operating on a respective one of the multiple phrase sets 763A-C.

The method usefully reports at least a subset of likely phrase occurrences 774, 776 and respective probability scores for further processing. The further processing may include sorting the likely phrase occurrences and presenting one or more of the likely phrase occurrences to a user for confirmation or selecting one or more items from a catalog based on the likely phrase occurrences. One or more selected catalog items can, in turn, be presented to the user.

In some implementations, ordered words and phrases can be organized into phrase graphs 763C. At lease some of the items in the catalog will be represented by multiple phrases with different word orderings. Phrase graphs represent all phrases for which items from the catalog will be selected. The phrase graphs may include edges (FIG. 3, dashed line) that indicate words subject to deletion with corresponding penalties for deleting words.

As described above, likely word instances may be represented by a data structure that includes fields for starting frame, starting frame, last starting frame and probability of a match. Where the first ending frame and last ending frame differ, multiple probabilities may be recorded in the data structure.

The processing of successive likely word instances may include splitting a sound between successive word instances. This may happen when a particular sound occurs at the end of one word and the beginning of the next word. Overlapping between likely word instances can be avoided by splitting a sound between two word instances, eliminating overlap. The difference between a first end frame and a last end frame can be used to handle the splitting.

Some phrases in the dictionary can be eliminated from consideration because they start with one or more words that are not found in a subset of likely word instances.

The phrase spotting method described immediately above need not be practiced successively with the word spotting method described. It is useful with a wide variety for spotting methods. Instead of depending on the earlier method, it made be described independently, beginning with step of searching for and scoring a set of likely word instances in an utterance 624, 626, with overlap among likely word instances 621B. Then, the steps from dividing the catalog of phrases 763A-C onward can proceed, without depending on any particular word spotting method.

A variety of computer devices and systems can practice the methods described above. One device is a system that electronically processes an utterance to locate candidate words at arbitrary positions within the utterance. It includes first multiple processors 615A-C and memory coupled to the processors. It further includes a dictionary of word representations 613 stored in first memory 611, the dictionary divided into multiple sets 613A-C.

A word searching-scoring module 617A processes a particular word set 613A. This includes searching and scoring. It searches the utterance 621A stored in a second memory for likely instances of each word representation at locations 624, 625 in the utterance that overlap in time 621B and scores each likely word instance for probability of a match between the word representation and a particular location in the utterance.

A first coordination module 619 is adapted to assign multiple instances of the word searching-scoring modules 617A-C to run on the first multiple processors 615A-C, each word searching-scoring module 617A-C assigned to search and score using respective one of the multiple word sets 613A-C.

The first reporting module (not shown) is coupled in communication with the first coordination module 619 and/or the word searching-scoring modules 617A-C. It reports at least a subset of likely word instances 621B and respective probability scores for further electronic processing. As described above, the further electronic processing may be accomplished by a successive phrase-spotting component (FIG. 7) coupled to the word spotter (FIG. 6).

The phrase spotter is a computer system that may accept input from the word spotting system described above (FIG. 6) or from any other word spotting system. This phrase spotting system may process the likely word instances 621B reported by the word spotter against catalog entries 761. It includes second multiple processors 765A-C coupled to the memory. The second multiple processors 765A-C may be coincident with, overlap with or be distinct from the first multiple processors 617A-C of the word spotter.

The phrase spotter includes a catalog of phrases 763 that represent items, the catalog divided into multiple phrase sets 763A-C. The catalog is stored in a third memory 761. It further includes of phrase searching-scoring module 767A that processes a particular phrase set 763A. This module is adapted to search and score. It searches the reported subset of likely word instances 621B for likely occurrences of each phrase and scores each likely phrase occurrence 774, 776 for probability the match between the phrase and the utterance 621A.

A second coordination module 769 is adapted to assign multiple instances of the phrase searching-scoring modules 767A-C to run on the second multiple processors 765A-C. Each phrase searching-scoring module 767A-C is assigned to search and score a respective one of the multiple phrase sets 763A-C.

A second reporting module (not illustrated) is coupled in communication with the second coordination module 769 and/or the phrase searching-scoring modules 767A-C. It reports at least a subset of likely phrase occurrences 774, 776 and respective probability scores for further processing. The further processing maybe as described in the methods above.

Without repeating the features, aspects and options of the methods described above, one of skill in the art will understand that the two systems described above may optionally include any of the features of the methods described earlier. The features, aspects and options of the methods should be treated as if they were multiply dependent claims modifying an omnibus system embodiment of the first method and of the second method, the second method both practiced by itself and in combination with the first method.

An article of manufacture may include a computer readable storage media that includes computer instructions. The computer instructions either may implement one of the methods above, or they may be designed to create one of the devices described when the computer instructions are installed on a system with multiple processors and memory. All of the features of the methods and devices may optionally be included among computer instructions on these articles of manufacture.

The technology disclosed addresses the problem with electronically processing an utterance to locate candidate words at arbitrary positions within the utterance, as illustrated in FIG. 6. An alternative way of viewing the technology disclosed includes the following method:

Dividing a dictionary 613 of representations of words into parts 613A-C and processing the parts in parallel on multiple processors 615A-C. The dictionary may be stored in a rotating or non-rotating memory 611.

Comparing and scoring the representations of the words to the utterance 621A at locations substantially throughout the utterance, using the processors 615A-C, wherein the scoring treats words independently at least during a first pass and treats scoring of a word at non-overlapping locations independently scoring that word at other locations. This produces candidate words scored for a probability that the candidate words appear at the locations in the utterance.

Reporting at least a subset of candidate words at locations (624, 626 in 621B) based on the parallel processed, independently scored word-location comparisons.

In some implementations, the sound-based representations include phonemes. These phonemes, in turn, can be represented by hidden Markov models (FIGS. 2, 4). The hidden Markov models may include three or more states per phoneme. Phonemes may be combined into triphones that improve the accuracy of representing words as successive phonemes. Representation of the phonemes as hidden Markov models is entirely optional, as there are a wide variety of technologies for extracting phonemes from a series of audio samples.

The sound-based representations of the words may be hidden Markov models, without intermediate phoneme representations.

The parallel processing may be further subdivided and allocated to separate processors by dividing the utterance into two or more overlapping parts 631, 633 and assigning the parts for parallel processing on multiple processors 615A-C.

The dictionary includes thousands of words. Typically, it has more than 10,000 words and often in the range of 60,000 to 100,000 words. With current processors, the dictionary preferably is less than 500,000 words. With future processors or implementations on GPU's, dictionaries of 1 million words and more will be practical. To accommodate multiple speakers, dictionaries may have multiple phonetic representations of the same word or multiple dictionaries that match different patterns of pronunciation.

A partial solution to the problem of using spoken utterances to select items from a catalog is illustrated in FIG. 7. It includes:

Finding candidate words 624, 626 at arbitrary locations throughout an utterance 621B, with overlap among the candidate words. This may be done using the method illustrated in FIG. 6 or any other word spotting method chosen.

Using a catalog 763A-C that identifies items by ordered word phrases 763C, the phrases including multiple words and alternate word orderings that identify the items. The catalog may be stored in a rotating or non-rotating memory 761.

Dividing the catalog into parts 763A-C and processing the parts in parallel on multiple processors 765A-C,

Comparing and scoring the phrases in the catalog (e.g., 763C) to the candidate words (e.g., 624) in the utterance 621B using the multiple processors 765A-C, wherein the scoring eliminates from consideration any phrases that are anchored by word(s) not found in the utterance and scoring remaining phrases beginning at locations (e.g., 624) in the utterance where the anchoring candidate words are located. Those with skill in the art will understand that the anchor can be at the beginning or end of a phrase with little computational difference. Likely phrase occurrences are scored for a probability that the phrase matches the utterance.

A set of likely phrase occurrences is reported for further processing.

This method may further include applying dynamic programming to score ordered, non-overlapping sequences of the candidate words 774, 776 against the phrases. It may include any of the phrase spotting features, aspects or options described above.

A device embodiment, from the word spotting or phrase spotting perspective, may be embodied in a device including multiple processors, memory coupled to the processors, one or more ports coupled to the processor (or the memory) adapted to word spotting and/or phrase spotting. Features, aspects and options of the devices described above can be combined with this alternative device embodiment, as if they were multiply dependent claims related to an omnibus system claim.

While the present invention is disclosed by reference to the preferred embodiments and examples detailed above, it is understood that these examples are intended in an illustrative rather than in a limiting sense. Computer-assisted processing is implicated in the described embodiments. Accordingly, the present invention may be embodied in methods for word and/or phrase spotting, systems including logic and resources to carry out word and/or phrase spotting, systems that take advantage of computer-implemented word and/or phrase spotting, media impressed with logic to carry out word and/or phrase spotting, data streams impressed with logic to carry out word and/or phrase spotting, or computer-accessible services that carry out computer-assisted word and/or phrase spotting. It is contemplated that modifications and combinations will readily occur to those skilled in the art, which modifications and combinations will be within the spirit of the invention and the scope of the following claims. 

1. A method of electronically processing an utterance to locate candidate words at arbitrary positions within the utterance, including: dividing a dictionary of word representations into multiple word sets; for a particular word set; searching the utterance for likely instances of each word representation at locations in the utterance that overlap in time and scoring each likely word instance for a probability of a match between the word representation and a particular location in the utterance; wherein, the utterance is searched by multiple processors each operating on a respective one of the multiple word sets; and reporting at least a subset of likely word instances and respective probability scores for further electronic processing.
 2. The method of claim 1, wherein each likely word instance is represented by at least a start frame, a first end frame, a last end frame and multiple probability scores when the first end frame and the last end frame are different.
 3. The method of claim 1, further including applying dynamic programming to hidden Markov models of the utterance and of the word representation during the searching and/or scoring.
 4. The method of claim 3, wherein the hidden Markov models represent phonemes.
 5. The method of claim 3, wherein the hidden Markov models for the word representations model words without intermediate phoneme representations.
 6. The method of claim 1, further including applying dynamic programming to phoneme representations of the utterance and the word representation during the searching and/or scoring.
 7. The method of claim 6, further including constructing the phoneme representations for the word representations as one or more phoneme graphs.
 8. The method of claim 7, wherein the scoring uses probabilities derived from training the phoneme graphs against audio data and not probabilities derived from a language model.
 9. The method of claim 6, further including analyzing the utterance against one or more phoneme graphs that represent allowable transitions between successive phonemes, wherein the allowable transitions are constrained by a language model based on the word representations in the dictionary.
 10. The method of claim 9, further including training the phoneme graphs against audio data.
 11. The method of claim 10, wherein the trained phoneme graphs represent alternative pronunciations by more than 100 speakers.
 12. The method of claim 1, further including reporting for further processing at least three times as many likely word instances as there are distinct words in the dictionary.
 13. The method of claim 3, wherein the scoring further includes normalizing partial scores accumulated while applying the dynamic programming, whereby the scores for the likely word instances can be meaningfully compared between the likely word instances that match shorter and longer parts of the utterance.
 14. The method of claim 6, wherein the scoring further includes normalizing partial scores accumulated while applying the dynamic programming, whereby the scores for the likely word instances can be meaningfully compared between the likely word instances that match shorter and longer parts of the utterance.
 15. The method of claim 1, applied to matching the likely word instances to catalog entries, the method further including: dividing a catalog of phrases that represent items into multiple phrase sets; for a particular phrase set, searching the reported subset of likely word instances for likely occurrences of each phrase and scoring each likely phrase occurrence for a probability of a match between the phrase and the utterance; wherein the subset of likely word instances is searched by multiple processors each operating on a respective one of the multiple phrase sets; and reporting at least a subset of likely phrase occurrences and respective probability scores for further processing.
 16. The method of claim 15, wherein the phrases include ordered words organized into one or more phrase graphs.
 17. The method of claim 16, wherein at least some of the items in the catalog are represented by multiple phrases with different word orderings.
 18. The method of claim 17, wherein the phrase graphs represent all phrases for which items from the catalog will be selected.
 19. The method of claim 17, wherein the phrase graphs include edges that indicate deleted words and penalties associated with deleting words from phrases.
 20. The method of claim 15, wherein each likely word instance is represented by at least a start frame, a first end frame, a last end frame and multiple probability scores when the first end frame and the last end frame are different, further including using overlap between likely word instances when searching for the phrase to eliminate from consideration some of the likely word instances.
 21. The method of claim 15, further including applying dynamic programming to scoring the phrases against ordered, non-overlapping sequences of the likely word instances.
 22. The method of claim 21, further including splitting a sound in the utterance shared by successive likely word instances, thereby eliminating overlap.
 23. The method of claim 21, further including eliminating from consideration phrases that start with one or more words not found in the subset of likely word instances.
 24. The method of claim 23, further including splitting a sound in the utterance shared by successive likely word instances, thereby eliminating overlap.
 25. A method of matching candidate words in an utterance to catalog entries, the method including: searching for and scoring a set of likely word instances in the utterance, with overlap among the likely word instances; dividing a catalog of phrases that represent items into multiple phrase sets; for a particular phrase set, searching the set of likely word instances for likely occurrences of each phrase and scoring each likely phrase occurrence for a probability of a match between the phrase and the utterance; wherein the set of likely word instances is searched by multiple processors each operating on a respective one of the multiple phrase sets; and reporting at least a subset of likely phrase occurrences and respective probability scores for further processing.
 26. A device that of electronically processes an utterance to locate candidate words at arbitrary positions within the utterance, including: first multiple processors; memory coupled to the processors; a dictionary of word representations stored a first memory, the dictionary divided into multiple word sets; a word searching-scoring module that processes a particular word set, the word searching-scoring module adapted to search the utterance stored in a second memory for likely instances of each word representation at locations in the utterance that overlap in time and score each likely word instance for a probability of a match between the word representation and a particular location in the utterance; further including a first coordination module adapted to assign multiple instances of the word searching-scoring module to run on the first multiple processors, each word searching-scoring module assigned to search and score using a respective one of the multiple word sets; and a first reporting module coupled in communication with the first coordination module and/or the word searching-scoring modules that reports at least a subset of likely word instances and respective probability scores for further electronic processing.
 27. The device of claim 26, extended to process the likely word instances against catalog entries, including: second multiple processors coupled to the memory; a catalog of phrases that represent items, stored a third memory, the catalog divided into multiple phrase sets; a phrase searching-scoring module that processes a particular phrase set, the phrase searching-scoring module adapted to search the reported subset of likely word instances for likely occurrences of each phrase and score each likely phrase occurrence for a probability of a match between the phrase and the utterance; further including a second coordination module adapted to assign multiple instances of the phrase searching-scoring module to run on the second multiple processors, each phrase searching-scoring module assigned to search and score using a respective one of the multiple phrase sets; and a second reporting module coupled in communication with the second coordination module and/or the phrase searching-scoring modules that reports at least a subset of likely phrase occurrences and respective probability scores for further processing.
 28. An article of manufacture including a computer readable storage media containing at least instructions to carry out a method of electronically processing an utterance to locate candidate words at arbitrary positions within the utterance, including: dividing a dictionary of word representations into multiple word sets; for a particular word set; searching the utterance for likely instances of each word representation at locations in the utterance that overlap in time and scoring each likely word instance for a probability of a match between the word representation and a particular location in the utterance; wherein, the utterance is searched by multiple processors each operating on a respective one of the multiple word sets; and reporting at least a subset of likely word instances and respective probability scores for further electronic processing.
 29. An article of manufacture including a computer readable storage media containing at least instructions to carry out a method of matching candidate words in an utterance to catalog entries, the method including: searching for and scoring a set of likely word instances in the utterance, with overlap among the likely word instances; dividing a catalog of phrases that represent items into multiple phrase sets; for a particular phrase set, searching the set of likely word instances for likely occurrences of each phrase and scoring each likely phrase occurrence for a probability of a match between the phrase and the utterance; wherein the set of likely word instances is searched by multiple processors each operating on a respective one of the multiple phrase sets; and reporting at least a subset of likely phrase occurrences and respective probability scores for further processing. 